Regional patterns and drivers of total nitrogen trends in the Chesapeake Bay watershed: Insights from machine learning approaches and management implications
Nutrient enrichment is a major issue to many inland and coastal waterbodies worldwide, including Chesapeake Bay. River water quality integrates the spatial and temporal changes of watersheds, thus monitoring forms the foundation for disentangling the effects of anthropogenic inputs. We demonstrate with the Chesapeake Bay Non-Tidal Monitoring Network (84 stations) that advanced machine learning approaches – i.e., hierarchical clustering and random forest – can be combined to better understand the regional patterns and drivers of total nitrogen (TN) trends in large monitoring networks. Cluster analysis revealed the regional patterns of short-term TN trends (2007-2018) and categorized the stations to three distinct clusters, namely, V-shape (n = 25), monotonic decline (n = 35), and monotonic increase (n = 26). Random forest models were developed for these clusters, which provided important information on regional drivers of TN trends. We show encouraging evidence that improved nutrient management has contributed to water-quality improvement. In addition, water-quality improvements are more likely in watersheds underlain by carbonate rocks, reflecting the relatively quick groundwater transport of this terrain. By contrast, water-quality improvements are less likely in watersheds in the Coastal Plain, reflecting the effect of legacy N in groundwater. Notably, TN trends are degrading in forested watersheds, which may compromise water-quality management efforts. TN trends were predicted for 979 subbasins across the entire Chesapeake Bay watershed, providing information that can facilitate targeted watershed management, especially in unmonitored areas. This research highlights the contribution of agricultural nutrient management in the Chesapeake Bay watershed toward reducing nitrogen loads.